Azure Functions Documentation

Monitor Azure Functions

This document provides comprehensive guidance on monitoring your Azure Functions applications to ensure performance, identify issues, and optimize resource utilization.

Introduction to Monitoring

Monitoring is a crucial aspect of managing any cloud-based application, and Azure Functions are no exception. Effective monitoring helps you understand how your functions are performing, detect errors, and diagnose problems before they impact your users. Azure provides powerful tools to help you achieve this.

Using Application Insights

Azure Application Insights is an extensible Application Performance Management (APM) service for developers. It can be used to automatically detect and help diagnose issues and to understand how users are interacting with your app. It helps you identify performance bottlenecks, track down exceptions, and understand usage patterns.

Setting up Application Insights

Application Insights can be easily integrated with your Azure Functions. When you create an Azure Function app, you can often choose to enable Application Insights directly. If not, you can add it later by configuring an Application Insights resource and setting the APPINSIGHTS_INSTRUMENTATIONKEY or APPLICATIONINSIGHTS_CONNECTION_STRING application setting in your Function App configuration.

{
  "IsEncrypted": false,
  "Values": {
    "AzureWebJobsStorage": "...",
    "FUNCTIONS_WORKER_RUNTIME": "dotnet",
    "APPINSIGHTS_INSTRUMENTATIONKEY": "YOUR_INSTRUMENTATION_KEY"
  }
}

Key Application Insights Features for Functions:

• Live Metrics Stream: Real-time monitoring of requests, dependencies, and exceptions.
• Failures Blade: Detailed analysis of exceptions and failed requests.
• Performance Blade: Identifying slow operations and performance bottlenecks.
• Server Response Time: Tracking the latency of your function executions.
• Alerts: Configuring rules to notify you of critical events or performance degradation.

Key Monitoring Metrics

Beyond Application Insights, Azure Monitor provides a wealth of metrics for your Function Apps:

• Function Execution Count: The total number of times a specific function has been executed.
• Function Execution Units: Measures the compute resources consumed by function executions.
• HTTP 5xx Errors: Indicates server-side errors occurring within your functions.
• HTTP 4xx Errors: Indicates client-side errors or issues with requests.
• Memory Working Set: The amount of physical memory currently used by your function process.
• CPU Time: The amount of CPU time consumed by your function executions.

These metrics can be viewed in the Azure portal under the "Monitor" section of your Function App and can be used to create custom dashboards and alerts.

Diagnosing Failures

When your functions encounter errors, diagnosing the root cause is paramount. Application Insights provides powerful tools for this:

• Exceptions: View detailed stack traces, request context, and associated telemetry for all exceptions thrown by your functions.
• Failures Blade: Group exceptions by type and analyze failure rates. This blade helps you quickly identify the most frequent or impactful errors.
• Log Analytics: Query rich logs generated by your functions and Application Insights for deeper investigation. You can correlate logs from different sources to pinpoint the exact sequence of events leading to a failure.

Example Log Analytics Query for Failures:

exceptions
| where timestamp > ago(1h)
| summarize count() by type, problemId, outerMessage
| order by count_ desc

Performance Tuning

Optimizing the performance of your Azure Functions can lead to cost savings and improved user experience. Key areas to focus on include:

• Cold Starts: Understand and mitigate the latency introduced by cold starts, especially for Consumption and Premium plans. Techniques like pre-warming or using the Premium plan can help.
• Execution Duration: Analyze the performance blade in Application Insights to identify functions that are taking too long to execute. Optimize your code, external dependencies, and data access patterns.
• Concurrency: Understand how your functions handle concurrent requests and configure appropriate settings to avoid resource contention.
• Scaling: Monitor your function's scaling behavior and adjust host-level scaling settings if necessary to meet demand without over-provisioning.

Logging Best Practices

Effective logging is the backbone of successful monitoring. Follow these best practices:

• Log Informative Messages: Include context like input parameters, important state changes, and the outcome of operations.
• Use Appropriate Log Levels: Utilize levels like Information, Warning, and Error to categorize your logs.
• Include Correlation IDs: When dealing with distributed systems or multiple function calls within a single request, use correlation IDs to track a request across different components. Application Insights often handles this automatically.
• Avoid Logging Sensitive Data: Be mindful of security and privacy. Do not log personally identifiable information (PII) or sensitive credentials directly.
• Structured Logging: Consider using structured logging (e.g., JSON format) to make logs easier to parse and query in tools like Log Analytics.